Intel Software’s Post

Intel AI-PC is the best of your productivity.. You can run AI workload on your notebook powered by Intel Core Ultra Processor Here is the simple model optimized RAG (Retrieval-augmented generation) using OpenVINO and LangChain leveraging AI capabilities of xPU on Intel Core Ultra AI PC. While the general purposed LLM Penalize latency and data privacy, RAG enhances the accuracy and reliability of generative AI models using local data source. #iamintel #AIPC #Performance

While LLMs and the applications built around them have emerged as powerful tools for understanding and generating natural language, optimizing these models for maximum efficiency and performance still poses a significant challenge. On May 8, join Intel as we host a webinar covering how to optimize #LLM workloads on target hardware using the Intel Extensions for Transformers and PyTorch. Register here: https://intel.ly/4a8RFkz #intel #CPU_and_GPU #pytorch_Transformers

NVIDIA has launched Llama-3.1-Nemotron-70B-Instruct, a large language model designed to enhance the quality of generated responses. This model was trained using 21,362 prompt-response pairs, aiming to align outputs with human preferences, specifically focusing on helpfulness, factual accuracy, coherence, and customizability in terms of complexity and verbosity. Of these, 20,324 pairs were utilized for training, while 1,038 pairs were reserved for validation. The model supports an input of up to 128k tokens and can generate outputs of up to 4k tokens.

Thanks to the optimizations done by Intel Corporation with the OpenVINO toolkit, you can run high performance, low latency inference on a laptop CPU with great precision and accuracy. This toolkit makes it simple to integrate the API and pre-trained AI models into your own application. I'm looking forward to showcasing this and other Intel optimized software at the D&H Distributing THREAD event. Stop by and check it out! #AI

Marvell Technology - Aug 25, 2023 AI training and inference require inordinate resources. And specialized chips are far better at it than CPUs. Chris Koopmans discusses with Futurum’s Daniel Newman how AI is driving the trend toward specialized processors. CPUs solve a broad set of problems. Specialized processors don’t, but they can solve the ones they were designed for more efficiently and rapidly. #ConfidentialComputing #AITraining #Inference #DataSecurity #NationalSecurity #ChipsAct #MarvellTechnology

As models get larger and slower, it’s crucial to learn how to optimize them for Intel hardware with dynamic quantization, weight compression, and KV caching. Watch the full video to learn how to develop fast and efficient AI models in just a few minutes. https://intel.ly/3zQbnpc #Developer #Software #Artificialintelligence

FDA must reconsider permission to the use of Neuralink's BCI. Neuralink has slipped or skipped the use of safest module with contextual domain. I know they don't have it, not even the Walts. Then why to risk lives? Just wait, until I bring it to them after DARPA approval. My device can perceive the Quantum fields, as it has unique contextual domain-based Walt-1. A leap in tech that goes beyond the standard approach. #QuantumField #Innovation #Walt1 #Nvidia #UZ-Tech #consciousnoblereceptors enjoy...

Named Entity Recognition (NER) projects are infamously tricky to get right - especially at scale. Astutic Artificial Intelligence has the answers. They built CleanML, a data-centric MLOps tool that streamlines annotation and model comparison, bringing everything a machine learning team needs into one platform. Keep reading to find out how this exciting Intel Liftoff member is improving NER with LLMs and Intel hardware: https://intel.ly/3Aq3xmE #IntelLiftoff #MLOps #MachineLearning

Change is the only constant in life. Dell Technologies #PowerEdge #XE9680 server with AMD #Instinct #MI300X accelerator and #ROCm 6 open software platform is change that is adding #choice and #competition for #AI and #GenAI. AMD - Change for the better. https://lnkd.in/grqNPC6C

In #SNUGIsrael's second keynote presentation, Google’s Uri Frank explained why the AI-era silicon challenges are much different than we had in the past. For start, AI models require a x10 speedup in compute power every year, much more than Moore's law.   Therefore, the current design flow that requires an average of 3 years for a new major device won’t cut. By the time these chips are ready, the workloads they were planned for are no longer relevant! Uri said that an #evolution won’t be enough- like the #AI-based small improvements offered by the EDA vendors. We need a #revolution - we must define new ways of designing chips:  “What if designing a custom chip took a few people a few weeks?” he asked. Synopsys Users Group (SNUG)